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Machine Learning with Python

Build algorithms that learn from data. From linear regression to neural network basics — a practitioner-first ML course covering every technique that appears in data science job descriptions.

⏱ 2 Months 🖥 Online & Offline 🎓 Certificate Included 💼 100% Placement Assistance

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Tools & Technologies

The ML Stack You'll Work With

Hands-on practice with the libraries and frameworks that appear in every data science and ML job description.

🐍

Python

📐

Scikit-Learn

🚀

XGBoost

🧮

TensorFlow (Intro)

📊

Matplotlib

Is This Course For You?

Who Should Enroll

This course is built for those with a Python foundation who are ready to move into machine learning.

📊

Python Analysts

Already comfortable with Pandas/EDA and ready for the next level.

🎓

Data Science Aspirants

Want the algorithms background that makes a data scientist different from an analyst.

💻

Engineers & CS Grads

Strong programming foundations but no ML exposure yet.

🔄

Working Professionals

In analytics, finance, or product and want to add ML to your toolkit.

Detailed Curriculum

Machine Learning Course Syllabus

Comprehensive Training Breakdown — Algorithms, Model Building & Deployment

Machine Learning

▼

Module 1Introduction to Machine Learning, ML Lifecycle, Supervised vs Unsupervised Learning
Module 2Data Preprocessing, Handling Missing Values, Feature Scaling, Encoding Categorical Variables
Module 3Linear Regression & Multiple Linear Regression, Cost Function, Gradient Descent
Module 4Logistic Regression, Sigmoid Function, Binary & Multi-class Classification
Module 5Decision Trees & Random Forest Algorithms, Information Gain, Gini Impurity
Module 6Support Vector Machines (SVM) & Naive Bayes Classifier
Module 7K-Means Clustering, Hierarchical Clustering & Elbow Method
Module 8Dimensionality Reduction Techniques: Principal Component Analysis (PCA) & LDA
Module 9Model Evaluation Metrics: Confusion Matrix, Precision, Recall, F1-Score, ROC-AUC Curve
Module 10Ensemble Learning: Gradient Boosting, XGBoost, AdaBoost
Module 11Introduction to Neural Networks, Perceptron, Artificial Neural Networks (ANN)
Module 12ML Model Deployment using Flask/FastAPI & REST API Integration

Career Outcomes

Roles Open to ML Graduates

Machine learning skills are in high demand — from startups to enterprise tech companies across India.

🤖 Machine Learning Engineer

₹8 – 18 LPA

Jio, PhonePe, Paytm, CRED, Razorpay, Google

🔬 Data Scientist

₹7 – 16 LPA

Fractal Analytics, Mu Sigma, Latent View, Tiger Analytics, WNS

📊 ML Analyst

₹6 – 12 LPA

TCS, Wipro, Infosys, Capgemini, IBM, Accenture

🧬 Research Analyst (AI)

₹8 – 20 LPA

Amazon, Flipkart, Myntra, Ola, Urban Company, Meesho

Enroll Today

Fee, Batches & Centres

Transparent pricing. Seats are limited per batch — reserve yours early.

₹12,000 + Tax

* All study materials, projects & certificate included

Duration2 Months

ModeOnline & Offline

Batch SizeMax 20 Students

CertificateThe XL Academy

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Upcoming Batches

Weekday BatchMon to Fri — 10:00 AM to 6:00 PM

Starting Soon

Weekend BatchSat & Sun — 10:00 AM to 6:00 PM

Few Seats Left

Online BatchFlexible — recorded backups included

Open

Available Centres

📍 New Delhi (HQ) 📍 Gurugram 📍 Noida 📍 Bangalore 📍 Pune 📍 Mumbai 🌐 Online

Market Opportunity

Machine Learning Is Transforming Industry

From predictive analytics to AI models, machine learning expertise commands top tech compensation globally.

₹7 – ₹18 LPA

Typical salary range for ML Engineers in India

40%

YoY surge in Machine Learning job openings

35K+

Active ML roles across Indian tech hubs

4.2x

Higher demand than traditional software roles

95%

Placement success rate for ML program graduates

Common Questions

Machine Learning — FAQs

Do I need to know Python before this course?

Yes, basic Python is recommended. If you're starting fresh, consider our Python for Data Analytics module first, or join the combined Data Science with ML course.

Is this the same content as the Data Science with ML course?

The ML content is similar. The Data Science course is more comprehensive — it covers Python, Statistics, Excel, and SQL alongside ML. This focused module is for those who already have a Python/analytics foundation.

What ML libraries will I learn?

Primarily Scikit-Learn, with XGBoost, Matplotlib, Seaborn, Pandas, and NumPy. We introduce TensorFlow Keras for neural network basics.

Will I build projects I can add to my portfolio?

Yes — three capstone projects: customer churn prediction, house price regression, and customer segmentation. All use real datasets and are formatted for your GitHub/LinkedIn portfolio.

What salary can I expect after this course?

Entry-level ML/data science roles typically start at ₹6–8 LPA. With 1–2 years experience, ₹10–16 LPA is common. Salaries depend on company, city, and your Python foundation.